A determination apparatus extracts a plurality of specific events that have values greater than an event determination threshold from among a plurality of events that have occurred in chronological order. The determination apparatus generates a feature amount related to adjacent occurrence intervals of the plurality of specific events, using the plurality of specific events. The determination apparatus generates array data corresponding to the plurality of events using points each having components of the event determination threshold and the feature amount, while changing the event determination threshold. The determination apparatus determines a type of the plurality of events using the array data.

Provided are methods and apparatuses for determining a target object in an image based on an interactive input. A target object determining method acquires first feature information corresponding to an image and second feature information corresponding to an interactive input; and determines a target object corresponding to the interactive input from among objects in the image based on the first feature information and the second feature information.

The disclosure provides a visual positioning method and apparatus, an electronic device and a computer-readable storage medium. The method includes: generating a semantic graph by semantically identifying collected images; determining description information of each entity through a random walk algorithm in the established semantic graph; determining candidate entities matching each entity in a preset entity map based on the description information; and positioning a collection area of the current image based on an area where the candidate entities are located in the preset entity map. The description information of each entity node constructed with the random walk algorithm not only contains semantic information of the corresponding node, but also local constraint information between semantics.

A computer-implemented method (100) for generating feedback based on a handwritten text, comprises the steps of initializing (110) a writing instrument (10) to be used in a writing operation comprising a handwritten text and capturing and processing (120) the handwritten text to generate digital text data. The method further comprises the steps of identifying (130) at least one handwritten text attribute associated with the digital text data, comparing (140) the at least one handwritten text attribute with predefined textual feature attributes, and generating (150) a textual feature based on the compared at least one handwritten text attribute and predefined textual feature attributes. In addition, the method comprises the steps of modifying (160) the digital text data using the textual feature and generating (170) feedback to a user (U) based on the modified digital text data.

The embodiments of this disclosure disclose an image object detection method, device, electronic equipment, and computer-readable medium. A specific mode of carrying out the method includes: performing region segmentation on a target image to obtain at least one image region; performing feature extraction on each image region in the at least one image region to obtain at least one feature map; generating a semantic relation graph and a spatial distribution relation graph based on the at least one feature map and the at least one image region; generating an image region relation graph based on the semantic relation graph and spatial distribution relation graph; determining a target image region from the at least one image region based on the image region relation graph; displaying the target image region. This implementation mode achieves an improvement of user experience and a growth of network traffic.

The embodiments of this disclosure disclose an image object detection method, device, electronic equipment, and computer-readable medium. A specific mode of carrying out the method includes: performing region segmentation on a target image to obtain at least one image region; performing feature extraction on each image region in the at least one image region to obtain at least one feature map; generating a semantic relation graph and a spatial distribution relation graph based on the at least one feature map and the at least one image region; generating an image region relation graph based on the semantic relation graph and spatial distribution relation graph; determining a target image region from the at least one image region based on the image region relation graph; displaying the target image region. This implementation mode achieves an improvement of user experience and a growth of network traffic.

A method for classifying subject activities in videos includes learning latent (previously generated) concepts that are analogous to nodes of a graph to be generated for an activity in a video. The method also includes receiving video segments of the video. A similarity between the video segments and the previously generated concepts is measured to obtain segment representations as a weighted set of latent concepts. The method further includes determining a relationship between the segment representations and their transitioning pattern over time to determine a reduced set of nodes and/or edges for the graph. The graph of the activity in the video represented by the video segments is generated based on the reduced set of nodes and/or edges. The nodes of the graph are represented by the latent concepts. Subject activities in the video are classified based on the graph.

A non-transitory computer readable medium (107, 127) stores instructions executable by at least one electronic processor (101, 113) to perform a component co-replacement recommendation method (200). The method includes: identifying components of a medical device by analyzing a technical document (130) related to the medical device; identifying component symbols (132) representing the components in drawings of the technical document; extracting relationships between the components of the medical device based on graphical connections (136) between the component symbols in the drawings of the technical document; generating a component connections graph (124) representing the relationships between the components of the medical device, the graph including nodes (138) corresponding to the components and connections (136) between the components; receiving an identification of a component to be replaced; and determining a co-replacement recommendation (122) for the component to be replaced based on the component connections graph.

A non-transitory computer readable medium (107, 127) stores instructions executable by at least one electronic processor (101, 113) to perform a component co-replacement recommendation method (200). The method includes: identifying components of a medical device by analyzing a technical document (130) related to the medical device; identifying component symbols (132) representing the components in drawings of the technical document; extracting relationships between the components of the medical device based on graphical connections (136) between the component symbols in the drawings of the technical document; generating a component connections graph (124) representing the relationships between the components of the medical device, the graph including nodes (138) corresponding to the components and connections (136) between the components; receiving an identification of a component to be replaced; and determining a co-replacement recommendation (122) for the component to be replaced based on the component connections graph.

The present disclosure herein provides methods and systems that solves the technical problems of generating an efficient, accurate and light-weight 3-Dimensional (3-D) pose estimation framework for estimating the 3-D pose of an object present in an image used for the 3-dimensional (3D) model registration using deep learning, by training a composite network model with both shape features and image features of the object. The composite network model includes a graph neural network (GNN) for capturing the shape features of the object and a convolution neural network (CNN) for capturing the image features of the object. The graph neural network (GNN) utilizes the local neighbourhood information through the image features of the object and at the same time maintaining global shape property through the shape features of the object, to estimate the 3-D pose of the object.